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Title

Computational classification of MocR transcriptional regulators into subgroups as a support for
experimental and functional characterization

 

Authors

Stefano Pascarella*

 

Affiliation

Structural bioinformatics and Molecular modelling Lab; Sapienza Università di Roma; 00185 Roma, Italy;

 

Email

Stefano Pascarella - E-mail: Stefano.Pascarella@uniroma1.it; Fax: +39 06 49917566; *Corresponding author

 

Article Type

Research Article

 

Date

Received January 15, 2019; Accepted February 3, 2019; Published February 28, 2019

 

Abstract

MocR bacterial transcriptional regulators are a subfamily within the GntR family. The MocR proteins possess an N-terminal domain containing the winged Helix-Turn-Helix (wHTH) motif and a C-terminal domain whose architecture is homologous to the fold type-I pyridoxal 5'-phosphate (PLP) dependent enzymes whose archetypical protein is aspartate amino transferase (AAT). The ancestor of the fold type-I PLP dependent super-family is considered one of the earliest enzymes. The members of this super-family are the product of evolution which resulted in a diversified protein population able to catalyze a set of reactions on substrates often containing amino groups. The MocR regulators are activators or repressors of gene control within many metabolic pathways often involving PLP enzymes. This diversity implies that MocR specifically responds to different classes of effector molecules. Therefore, it is of interest to compare the AAT domains of MocR from six bacteria phyla. Multi dimensional scaling and cluster analyses suggested that at least three subgroups exist within the population that reflects functional specialization rather than taxonomic origin. The AAT-domains of the three clusters display variable degree of similarity to different fold type-I PLP enzyme families. The results support the hypothesis that independent fusion events generated at least three different MocR subgroups.

 

Keywords

MocR; Pyridoxal 5’-phosphate; Structural bioinformatics; Aspartate aminotransferase; Multidimensional scaling analysis; Cluster analysis

 

Citation

Pascarella, Bioinformation 15(2): 151-159 (2019)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.